TABLE III. Evaluation of Preprocessing on ADECO-CNN and CNN-Based Transfer Learning Models.

Method	Measure	Original	Normalized
VGG19	ACC	73.14 0.88	81.07 0.21
		SEN	66.13 0.98	82.15 0.04
		PRE	86.07 0.98	94.85 0.04
	SPE	69.35 0.05	84.29 0.38
GoogleNet	ACC	79.24 0.73	84.24 0.21
		SEN	66.15 0.31	87.40 0.75
		PRE	67.13 0.31	79.10 0.75
	SPE	71.24 0.98	89.11 0.14
ResNet	ACC	81.88 0.24	91.02 0.03
		SEN	82.22 0.05	89.10 0.15
		PRE	88.44 0.11	95.90 0.01
	SPE	86.77 0.92	96.50 0.12
ADECO-CNN	ACC	67.17 0.87	99.99 0.01
		SEN	81.13 0.52	99.96 0.04
		PRE	82.13 0.52	99.92 0.08
	SPE	79.24 0.11	99.97 0.03

ACC = Accuracy, SEN = Sensitivity, PRE = Precision, and SPE = Specificity.